Building an Early Technology Trend Prediction and Private Investment Opportunity Discovery Pipeline through AI-based Patent Data Analysis: Innovation Company Exploration Strategy Using LLMs and Graph Neural Networks

Traditional, ad-hoc methods of technology trend prediction and investment opportunity discovery have reached their limits. This article presents a concrete AI-based pipeline construction strategy that leverages LLMs (Large Language Models) and Graph Neural Networks (GNNs) to accurately predict early-stage innovative technology trends and proactively identify hidden private investment opportunities within the vast ocean of patent data.

1. The Challenge / Context: Finding Hidden Treasures in the Age of Information Overload

We live in a flood of vast information that pours out daily. In particular, patent data, which showcases the forefront of technological innovation, is a powerful indicator for predicting a company's R&D direction, core technological capabilities, and future growth engines. However, it is almost impossible for humans to manually analyze tens of thousands of patent documents published worldwide every day to gain meaningful insights. Early-stage technology trends often do not yet appear in mainstream media or market reports, and it is difficult to gauge the potential of private innovative companies due to a lack of traditional financial data.

These issues deepen information asymmetry, lead to missed early investment opportunities, and hinder innovative technology companies from securing timely funding. This article proposes a method to overcome these challenges by combining LLM's powerful text understanding capabilities with GNN's relational data analysis capabilities to build a